ON CRITICAL-POINT DETECTION OF DIGITAL SHAPES

作     者：ZHU, PF CHIRLIAN, PM 

作者机构：UNIV NEW ORLEANS DEPT ELECT ENGN NEW ORLEANS LA 70148 USA 

出 版 物：《IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE》 (IEEE Trans. Pattern Anal. Mach. Intell.)

年 卷 期：1995年第17卷第8期

页      面：737-748页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Paul M. Chirlian (S ‘49-A ‘52-M ‘56-Sm‘6 1-F ‘79) received the BEE  MEE and EngScd degrees from New York University in 1950  1952  and 1956  respectively. He was a member of the faculty of the Electrical Engineering Department of New York University from 1950 to 1960. In 1960 he joined the faculty of the Stevens Institute of Technology at the rank of associate professor. In 1965 he was appointed pro-fessor and in 1985 he was named Anson Wood Burchard Professor of Electrical Engineering. Since 1993 he has been professor and chair of the Electrical Engineering Depiut ment at the University of New Orleans. Dr. Chirlian is the author of 29 textbooks and over 70 technical papers. In addition to the IEEE  he is a member of Sigma Xi  Eta Kappa Nu -T~uB eta Pi  and the ASEE. He is a registered professional engineer in the states of New York  New Jersey  and Louisiana 

主　　题：FEATURE POINT DETECTION SHAPE REPRESENTATION SHAPE ANALYSIS FEATURE EXTRACTION DIGITIZED CONTOUR NONLINEAR ALGORITHM SHAPE RECOGNITION 

摘      要：In this paper, we present a nonlinear algorithm for critical point detection (CPD) of 2D digital shapes, The algorithm eliminates the problems arising from curvature approximation and Gaussian filtering in the existing algorithms, Based on the definition of critical level, we establish a set of criteria for the design of an effective CPD algorithm for the first time, By quantifying the critical level to the modified area confined by three consecutive pseudocritical points, a simple but very effective algorithm is developed. The comparison of our experimental results with those of many other CPD algorithms shows that the proposed algorithm is superior in that it provides a sequence of figures at every detail level, and each has a smaller integral error than the others with the same number of critical points, The experimental results on shapes with various complexities also show the algorithm is reliable and robust with regard to noise.